Grid Lifespan Enlargement for Assessment in Multihop Wireless Detector Facilities

In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.

The Current State and Future Directions of Modeling Thermosphere Density Enhancements During Extreme Magnetic Storms

Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite’s lifetime and orbital tracking become increasingly inaccurate or uncertain during magnetic storms. Given the planned increase of government and private satellite presence in LEO, the need for accurate density predictions for collision avoidance and lifetime optimization, particularly during extreme events, has become an urgent matter and requires comprehensive international collaboration. Additionally, long-term solar activity models and historical data suggest that solar activity will significantly increase in the following years and decades. In this article, we briefly summarize the main achievements in the research of thermosphere response to extreme magnetic storms occurring particularly after the launching of many satellites with state-of-the-art accelerometers from which high-accuracy density can be determined. We find that the performance of an empirical model with data assimilation is higher than its performance without data assimilation during all extreme storm phases. We discuss how forecasting models can be improved by looking into two directions: first, to the past, by adapting historical extreme storm datasets for density predictions, and second, to the future, by facilitating the assimilation of large-scale thermosphere data sets that will be collected in future events. Therefore, this topic is relevant to the scientific community, government agencies that operate satellites, and the private sector with assets operating in LEO.

A lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm

The demand for new energy will continue to expand as the environment changes and fossil energy decreases. However, the instability of new energy has slowed down the development of new energy. The joint use of new energy and energy storage modules effectively solves the shortcomings of new energy. The article proposed a lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm. Firstly the life model based on the battery capacity [Formula: see text], charging current [Formula: see text], and discharge current [Formula: see text] is built. Secondly, the deep learning method is used to improve the step length and speed change of artificial fish-school algorithm. Finally, the simulation platform detects the optimized parameters [Formula: see text]. The simulation results show that optimized parameters can help extend the life of the energy storage module.

Machine Learning-Based Optimized Hierarchical Routing Protocols for WSN Lifetime: A Review

In Wireless Sensor Networks, network lifetime optimization has challenging and significant issue. Subsequently, most of the existing works delineate several factors to improve the network lifetime: by decreasing the amount of the consumption of energy, reducing latency, load balancing, clustering, efficient data aggregating and by minimizing the data transmission delays. This paper provides a review of recent techniques and presents a Machine Learning-based Optimized Hierarchical Routing Protocols for WSN Lifetime. Research has been done, and reviews have been studied to explore the energy management schemes using optimized routing approach and Machine Learning Adaptability for WSN’s. Further, recommend future directions related to the Optimized Clustering Approaches to enhance wsn lifetime.